Project Summary: Tuberculosis (TB) is the leading cause of infectious disease deaths globally. With TB incidence currently decreasing by 2% annually, achieving the World Health Organization?s ENDTB goal of TB elimination by 2050 will require a substantial reworking of our TB control approach. Previous elimination strategies for other diseases have only been successful once spatial variation in disease incidence was identified and then locally relevant interventions implemented. TB elimination strategies require longitudinal TB cohorts that incorporate detailed spatial information. South Africa is an ideal setting for our work with both the second highest TB incidence globally and a central National Health Laboratory Service (NHLS) database of routinely-collected laboratory results. We propose to develop a ten-year national TB cohort with the ability to track patients for up to ten years, including spatial location data and HIV status, using national NHLS data. We hypothesize that NHLS data can be used to identify key metrics in the TB care cascade, at the facility level, and evaluate the impact of HIV treatment scale-up and the roll-out of new diagnostics on the TB epidemic in South Africa. In aim 1a we will use probabilistic record linkage to create unique patient identifiers in the NHLS data for all TB patients, allowing us to link an individual?s test results and track those confirmed cases spatially and temporally, and incorporating HIV test results. In aim 1b, we will identify key steps in the TB care cascade and augment our ten-year cohort with data from clinic chart reviews. In aim 1c, we will identify locations with gaps in specific care cascade steps, common patient movement patterns during TB treatment and identify locations with potential higher burden of disease based on molecular test variables. In aim 2 we will quantify the relationship between HIV treatment levels and TB incidence, at the facility level. In aim 3 we will evaluate the association between GeneXpert Ultra ?trace? results and repeat TB episodes to enhance the interpretation of these new diagnostic results. This contribution is significant because it will develop a national ten-year cohort of TB patients tracking them longitudinally and spatially during treatment, enabling local public health professionals to develop locally appropriate interventions to close gaps in the TB care cascade and understand the impact of interventions and new TB diagnostics. The proposed work is innovative because it will provide the first cohort of this kind in a high burden setting, with the longitudinal and spatial nature of these data allowing for unprecedented capture of TB care and the impact of interventions. This will allow policy makers to design locally-relevant interventions targeting specific gaps in TB care and highly mobile populations, preventing further spread and ultimately reducing disease and mortality due to TB. By using routinely collected laboratory data, our model adds minimal financial costs and can be adapted for similar TB high-burden, middle-income settings with large laboratory datasets.